Volunteer Summary

CONSORT Flow Diagram

Overall status

Characteristic

Overall1

Control1

Treatment1

time_point

1st

120

58

62

2nd

100

53

47

1n

Demographic information

Characteristic

N

Overall, N = 1201

control, N = 581

treatment, N = 621

p-value2

age

120

38.15 ± 17.06 (18 - 148)

39.90 ± 19.46 (18 - 148)

36.51 ± 14.44 (20 - 70)

0.279

gender

120

0.298

female

86 (72%)

39 (67%)

47 (76%)

male

34 (28%)

19 (33%)

15 (24%)

occupation

120

0.659

civil

6 (5.0%)

2 (3.4%)

4 (6.5%)

clerk

23 (19%)

9 (16%)

14 (23%)

homemaker

8 (6.7%)

3 (5.2%)

5 (8.1%)

manager

16 (13%)

9 (16%)

7 (11%)

other

11 (9.2%)

4 (6.9%)

7 (11%)

professional

15 (12%)

11 (19%)

4 (6.5%)

retired

4 (3.3%)

2 (3.4%)

2 (3.2%)

service

5 (4.2%)

2 (3.4%)

3 (4.8%)

student

30 (25%)

15 (26%)

15 (24%)

unemploy

2 (1.7%)

1 (1.7%)

1 (1.6%)

working_status

120

76 (63%)

37 (64%)

39 (63%)

0.919

marital

120

0.477

divorced

4 (3.3%)

1 (1.7%)

3 (4.8%)

married

27 (22%)

15 (26%)

12 (19%)

single

88 (73%)

41 (71%)

47 (76%)

widowed

1 (0.8%)

1 (1.7%)

0 (0%)

marital_r

120

0.689

married

27 (22%)

15 (26%)

12 (19%)

other

5 (4.2%)

2 (3.4%)

3 (4.8%)

single

88 (73%)

41 (71%)

47 (76%)

education

120

0.074

primary

0 (0%)

0 (0%)

0 (0%)

secondary

14 (12%)

3 (5.2%)

11 (18%)

post-secondary

20 (17%)

12 (21%)

8 (13%)

university

86 (72%)

43 (74%)

43 (69%)

university_edu

120

86 (72%)

43 (74%)

43 (69%)

0.561

family_income

120

0.541

0_10000

13 (11%)

5 (8.6%)

8 (13%)

10001_20000

22 (18%)

8 (14%)

14 (23%)

20001_30000

23 (19%)

11 (19%)

12 (19%)

30001_40000

20 (17%)

10 (17%)

10 (16%)

40000_above

42 (35%)

24 (41%)

18 (29%)

high_income

120

62 (52%)

34 (59%)

28 (45%)

0.140

religion

120

0.649

buddhism

5 (4.2%)

4 (6.9%)

1 (1.6%)

catholic

5 (4.2%)

2 (3.4%)

3 (4.8%)

christianity

47 (39%)

23 (40%)

24 (39%)

nil

61 (51%)

29 (50%)

32 (52%)

other

1 (0.8%)

0 (0%)

1 (1.6%)

taoism

1 (0.8%)

0 (0%)

1 (1.6%)

religion_r

120

0.915

christianity

52 (43%)

25 (43%)

27 (44%)

nil

61 (51%)

29 (50%)

32 (52%)

other

7 (5.8%)

4 (6.9%)

3 (4.8%)

source

120

0.067

bokss

51 (42%)

20 (34%)

31 (50%)

facebook

17 (14%)

13 (22%)

4 (6.5%)

instagram

9 (7.5%)

6 (10%)

3 (4.8%)

other

19 (16%)

9 (16%)

10 (16%)

refresh

24 (20%)

10 (17%)

14 (23%)

1Mean ± SD (Range); n (%)

2Two Sample t-test; Pearson's Chi-squared test; Fisher's exact test

Measurement

Characteristic

N

Overall, N = 1201

control, N = 581

treatment, N = 621

p-value2

sets

120

19.20 ± 2.18 (15 - 25)

19.02 ± 2.03 (15 - 24)

19.37 ± 2.31 (15 - 25)

0.377

setv

120

11.14 ± 1.64 (7 - 15)

11.03 ± 1.56 (8 - 15)

11.24 ± 1.71 (7 - 15)

0.490

maks

120

44.92 ± 3.63 (36 - 57)

44.67 ± 3.59 (36 - 52)

45.16 ± 3.68 (38 - 57)

0.463

ibs

120

15.44 ± 2.45 (5 - 20)

15.41 ± 2.14 (10 - 20)

15.47 ± 2.72 (5 - 20)

0.904

ers_e

120

12.22 ± 1.46 (8 - 15)

12.14 ± 1.47 (8 - 15)

12.29 ± 1.45 (9 - 15)

0.569

ers_r

120

11.11 ± 1.58 (7 - 15)

11.02 ± 1.57 (7 - 14)

11.19 ± 1.59 (8 - 15)

0.543

pss_pa

120

44.62 ± 4.47 (30 - 54)

44.47 ± 4.26 (30 - 54)

44.76 ± 4.68 (31 - 54)

0.722

pss_ps

120

26.64 ± 8.34 (12 - 56)

26.67 ± 7.63 (13 - 42)

26.61 ± 9.02 (12 - 56)

0.969

pss

120

45.02 ± 11.85 (21 - 77)

45.21 ± 11.26 (22 - 72)

44.85 ± 12.47 (21 - 77)

0.872

rki_responsible

120

21.01 ± 4.13 (7 - 32)

20.95 ± 4.11 (13 - 29)

21.06 ± 4.18 (7 - 32)

0.878

rki_nonlinear

120

13.30 ± 2.75 (6 - 22)

13.12 ± 2.54 (6 - 20)

13.47 ± 2.94 (7 - 22)

0.492

rki_peer

120

20.58 ± 2.15 (16 - 25)

20.47 ± 2.07 (16 - 25)

20.68 ± 2.23 (16 - 25)

0.591

rki_expect

120

4.75 ± 1.09 (2 - 8)

4.60 ± 1.11 (2 - 8)

4.89 ± 1.07 (2 - 7)

0.157

rki

120

59.63 ± 6.10 (44 - 81)

59.14 ± 5.86 (45 - 76)

60.10 ± 6.33 (44 - 81)

0.392

raq_possible

120

15.66 ± 1.79 (12 - 20)

15.74 ± 1.89 (12 - 20)

15.58 ± 1.71 (12 - 20)

0.626

raq_difficulty

120

12.42 ± 1.39 (9 - 15)

12.53 ± 1.38 (9 - 15)

12.31 ± 1.41 (9 - 15)

0.373

raq

120

28.08 ± 2.90 (21 - 35)

28.28 ± 2.97 (21 - 35)

27.89 ± 2.85 (21 - 35)

0.466

who

120

14.63 ± 4.46 (3 - 25)

14.62 ± 4.24 (6 - 25)

14.65 ± 4.68 (3 - 25)

0.976

phq

120

3.76 ± 3.81 (0 - 18)

3.66 ± 3.73 (0 - 17)

3.85 ± 3.91 (0 - 18)

0.776

gad

120

3.23 ± 3.57 (0 - 21)

3.38 ± 4.11 (0 - 21)

3.08 ± 3.00 (0 - 12)

0.649

nb_pcs

120

51.64 ± 7.15 (25 - 63)

51.88 ± 7.17 (25 - 63)

51.42 ± 7.18 (27 - 62)

0.729

nb_mcs

120

50.24 ± 8.59 (22 - 70)

50.20 ± 8.89 (22 - 68)

50.28 ± 8.37 (35 - 70)

0.960

1Mean ± SD (Range)

2Two Sample t-test

Data analysis

Table

Group

Characteristic

Beta

SE1

95% CI1

p-value

sets

(Intercept)

19.0

0.269

18.5, 19.5

group

control

treatment

0.354

0.375

-0.381, 1.09

0.347

time_point

1st

2nd

-0.086

0.310

-0.693, 0.521

0.781

group * time_point

treatment * 2nd

0.381

0.446

-0.493, 1.26

0.394

Pseudo R square

0.019

setv

(Intercept)

11.0

0.219

10.6, 11.5

group

control

treatment

0.207

0.304

-0.389, 0.804

0.496

time_point

1st

2nd

0.318

0.215

-0.103, 0.738

0.142

group * time_point

treatment * 2nd

-0.238

0.310

-0.846, 0.369

0.444

Pseudo R square

0.006

maks

(Intercept)

44.7

0.487

43.7, 45.6

group

control

treatment

0.489

0.677

-0.838, 1.82

0.471

time_point

1st

2nd

-0.230

0.406

-1.03, 0.566

0.572

group * time_point

treatment * 2nd

0.044

0.589

-1.11, 1.20

0.940

Pseudo R square

0.006

ibs

(Intercept)

15.4

0.302

14.8, 16.0

group

control

treatment

0.054

0.420

-0.770, 0.877

0.898

time_point

1st

2nd

0.170

0.254

-0.328, 0.669

0.505

group * time_point

treatment * 2nd

0.399

0.369

-0.324, 1.12

0.282

Pseudo R square

0.010

ers_e

(Intercept)

12.1

0.186

11.8, 12.5

group

control

treatment

0.152

0.258

-0.354, 0.659

0.556

time_point

1st

2nd

-0.293

0.170

-0.627, 0.041

0.089

group * time_point

treatment * 2nd

0.381

0.247

-0.103, 0.864

0.126

Pseudo R square

0.019

ers_r

(Intercept)

11.0

0.195

10.6, 11.4

group

control

treatment

0.176

0.272

-0.356, 0.709

0.517

time_point

1st

2nd

0.100

0.237

-0.366, 0.565

0.676

group * time_point

treatment * 2nd

0.261

0.341

-0.408, 0.930

0.446

Pseudo R square

0.017

pss_pa

(Intercept)

44.5

0.580

43.3, 45.6

group

control

treatment

0.293

0.807

-1.29, 1.87

0.717

time_point

1st

2nd

-1.09

0.581

-2.23, 0.053

0.065

group * time_point

treatment * 2nd

0.533

0.840

-1.11, 2.18

0.527

Pseudo R square

0.014

pss_ps

(Intercept)

26.7

1.062

24.6, 28.8

group

control

treatment

-0.060

1.477

-2.95, 2.84

0.968

time_point

1st

2nd

0.982

0.861

-0.704, 2.67

0.256

group * time_point

treatment * 2nd

-1.59

1.247

-4.03, 0.856

0.206

Pseudo R square

0.005

pss

(Intercept)

45.2

1.509

42.2, 48.2

group

control

treatment

-0.352

2.099

-4.47, 3.76

0.867

time_point

1st

2nd

2.09

1.222

-0.303, 4.49

0.090

group * time_point

treatment * 2nd

-2.07

1.771

-5.54, 1.40

0.245

Pseudo R square

0.008

rki_responsible

(Intercept)

20.9

0.546

19.9, 22.0

group

control

treatment

0.116

0.759

-1.37, 1.60

0.879

time_point

1st

2nd

-0.015

0.476

-0.948, 0.917

0.975

group * time_point

treatment * 2nd

0.254

0.689

-1.10, 1.60

0.713

Pseudo R square

0.001

rki_nonlinear

(Intercept)

13.1

0.382

12.4, 13.9

group

control

treatment

0.347

0.531

-0.694, 1.39

0.514

time_point

1st

2nd

-0.297

0.352

-0.988, 0.393

0.400

group * time_point

treatment * 2nd

0.953

0.509

-0.045, 1.95

0.064

Pseudo R square

0.025

rki_peer

(Intercept)

20.5

0.287

19.9, 21.0

group

control

treatment

0.212

0.399

-0.570, 0.994

0.596

time_point

1st

2nd

0.029

0.273

-0.506, 0.565

0.914

group * time_point

treatment * 2nd

-0.091

0.395

-0.866, 0.683

0.818

Pseudo R square

0.002

rki_expect

(Intercept)

4.60

0.142

4.33, 4.88

group

control

treatment

0.284

0.197

-0.102, 0.670

0.151

time_point

1st

2nd

0.170

0.148

-0.121, 0.461

0.254

group * time_point

treatment * 2nd

0.109

0.214

-0.310, 0.529

0.610

Pseudo R square

0.033

rki

(Intercept)

59.1

0.823

57.5, 60.8

group

control

treatment

0.959

1.145

-1.28, 3.20

0.404

time_point

1st

2nd

-0.117

0.714

-1.52, 1.28

0.871

group * time_point

treatment * 2nd

1.25

1.033

-0.778, 3.27

0.230

Pseudo R square

0.018

raq_possible

(Intercept)

15.7

0.237

15.3, 16.2

group

control

treatment

-0.161

0.330

-0.807, 0.485

0.626

time_point

1st

2nd

-0.391

0.248

-0.876, 0.094

0.117

group * time_point

treatment * 2nd

0.696

0.357

-0.004, 1.40

0.054

Pseudo R square

0.011

raq_difficulty

(Intercept)

12.5

0.179

12.2, 12.9

group

control

treatment

-0.228

0.249

-0.716, 0.260

0.361

time_point

1st

2nd

-0.120

0.169

-0.451, 0.210

0.477

group * time_point

treatment * 2nd

0.214

0.244

-0.263, 0.692

0.381

Pseudo R square

0.004

raq

(Intercept)

28.3

0.381

27.5, 29.0

group

control

treatment

-0.389

0.530

-1.43, 0.650

0.464

time_point

1st

2nd

-0.497

0.357

-1.20, 0.202

0.167

group * time_point

treatment * 2nd

0.903

0.516

-0.108, 1.91

0.083

Pseudo R square

0.006

who

(Intercept)

14.6

0.590

13.5, 15.8

group

control

treatment

0.024

0.820

-1.58, 1.63

0.976

time_point

1st

2nd

-0.106

0.498

-1.08, 0.870

0.832

group * time_point

treatment * 2nd

0.060

0.721

-1.35, 1.47

0.934

Pseudo R square

0.000

phq

(Intercept)

3.66

0.491

2.69, 4.62

group

control

treatment

0.200

0.683

-1.14, 1.54

0.770

time_point

1st

2nd

0.118

0.337

-0.543, 0.778

0.728

group * time_point

treatment * 2nd

0.350

0.489

-0.610, 1.31

0.477

Pseudo R square

0.004

gad

(Intercept)

3.38

0.457

2.48, 4.28

group

control

treatment

-0.299

0.636

-1.54, 0.948

0.639

time_point

1st

2nd

-0.032

0.362

-0.741, 0.678

0.931

group * time_point

treatment * 2nd

0.430

0.525

-0.599, 1.46

0.414

Pseudo R square

0.002

nb_pcs

(Intercept)

51.9

0.920

50.1, 53.7

group

control

treatment

-0.455

1.280

-2.96, 2.05

0.722

time_point

1st

2nd

-0.787

0.766

-2.29, 0.714

0.307

group * time_point

treatment * 2nd

0.722

1.110

-1.45, 2.90

0.517

Pseudo R square

0.002

nb_mcs

(Intercept)

50.2

1.113

48.0, 52.4

group

control

treatment

0.080

1.548

-2.95, 3.11

0.959

time_point

1st

2nd

0.956

1.013

-1.03, 2.94

0.348

group * time_point

treatment * 2nd

-1.52

1.466

-4.39, 1.35

0.302

Pseudo R square

0.003

1SE = Standard Error, CI = Confidence Interval

Text

sets

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict sets with group and time_point (formula: sets ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.39) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st, is at 19.02 (95% CI [18.49, 19.55], t(214) = 70.57, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.35, 95% CI [-0.38, 1.09], t(214) = 0.94, p = 0.345; Std. beta = 0.17, 95% CI [-0.19, 0.53])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.09, 95% CI [-0.69, 0.52], t(214) = -0.28, p = 0.781; Std. beta = -0.04, 95% CI [-0.34, 0.25])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.38, 95% CI [-0.49, 1.26], t(214) = 0.86, p = 0.392; Std. beta = 0.19, 95% CI [-0.24, 0.61])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

setv

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict setv with group and time_point (formula: setv ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.55) and the part related to the fixed effects alone (marginal R2) is of 5.58e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 11.03 (95% CI [10.61, 11.46], t(214) = 50.46, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.21, 95% CI [-0.39, 0.80], t(214) = 0.68, p = 0.495; Std. beta = 0.12, 95% CI [-0.23, 0.48])
  • The effect of time point [2nd] is statistically non-significant and positive (beta = 0.32, 95% CI [-0.10, 0.74], t(214) = 1.48, p = 0.139; Std. beta = 0.19, 95% CI [-0.06, 0.44])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and negative (beta = -0.24, 95% CI [-0.85, 0.37], t(214) = -0.77, p = 0.442; Std. beta = -0.14, 95% CI [-0.51, 0.22])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

maks

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict maks with group and time_point (formula: maks ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.68) and the part related to the fixed effects alone (marginal R2) is of 5.68e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 44.67 (95% CI [43.72, 45.63], t(214) = 91.78, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.49, 95% CI [-0.84, 1.82], t(214) = 0.72, p = 0.470; Std. beta = 0.13, 95% CI [-0.23, 0.49])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.23, 95% CI [-1.03, 0.57], t(214) = -0.57, p = 0.571; Std. beta = -0.06, 95% CI [-0.28, 0.15])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.04, 95% CI [-1.11, 1.20], t(214) = 0.08, p = 0.940; Std. beta = 0.01, 95% CI [-0.30, 0.33])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

ibs

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict ibs with group and time_point (formula: ibs ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.67) and the part related to the fixed effects alone (marginal R2) is of 0.01. The model’s intercept, corresponding to group = control and time_point = 1st, is at 15.41 (95% CI [14.82, 16.01], t(214) = 51.04, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.05, 95% CI [-0.77, 0.88], t(214) = 0.13, p = 0.898; Std. beta = 0.02, 95% CI [-0.33, 0.38])
  • The effect of time point [2nd] is statistically non-significant and positive (beta = 0.17, 95% CI [-0.33, 0.67], t(214) = 0.67, p = 0.503; Std. beta = 0.07, 95% CI [-0.14, 0.29])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.40, 95% CI [-0.32, 1.12], t(214) = 1.08, p = 0.279; Std. beta = 0.17, 95% CI [-0.14, 0.49])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

ers_e

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict ers_e with group and time_point (formula: ers_e ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.62) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st, is at 12.14 (95% CI [11.77, 12.50], t(214) = 65.35, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.15, 95% CI [-0.35, 0.66], t(214) = 0.59, p = 0.555; Std. beta = 0.11, 95% CI [-0.25, 0.46])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.29, 95% CI [-0.63, 0.04], t(214) = -1.72, p = 0.086; Std. beta = -0.21, 95% CI [-0.44, 0.03])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.38, 95% CI [-0.10, 0.86], t(214) = 1.54, p = 0.123; Std. beta = 0.27, 95% CI [-0.07, 0.61])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

ers_r

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict ers_r with group and time_point (formula: ers_r ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.31) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st, is at 11.02 (95% CI [10.63, 11.40], t(214) = 56.42, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.18, 95% CI [-0.36, 0.71], t(214) = 0.65, p = 0.516; Std. beta = 0.12, 95% CI [-0.24, 0.48])
  • The effect of time point [2nd] is statistically non-significant and positive (beta = 0.10, 95% CI [-0.37, 0.56], t(214) = 0.42, p = 0.675; Std. beta = 0.07, 95% CI [-0.25, 0.38])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.26, 95% CI [-0.41, 0.93], t(214) = 0.76, p = 0.445; Std. beta = 0.17, 95% CI [-0.27, 0.62])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

pss_pa

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict pss_pa with group and time_point (formula: pss_pa ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.54) and the part related to the fixed effects alone (marginal R2) is of 0.01. The model’s intercept, corresponding to group = control and time_point = 1st, is at 44.47 (95% CI [43.33, 45.60], t(214) = 76.70, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.29, 95% CI [-1.29, 1.87], t(214) = 0.36, p = 0.717; Std. beta = 0.07, 95% CI [-0.29, 0.43])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -1.09, 95% CI [-2.23, 0.05], t(214) = -1.87, p = 0.062; Std. beta = -0.25, 95% CI [-0.50, 0.01])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.53, 95% CI [-1.11, 2.18], t(214) = 0.64, p = 0.525; Std. beta = 0.12, 95% CI [-0.25, 0.49])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

pss_ps

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict pss_ps with group and time_point (formula: pss_ps ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.70) and the part related to the fixed effects alone (marginal R2) is of 4.93e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 26.67 (95% CI [24.59, 28.75], t(214) = 25.12, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and negative (beta = -0.06, 95% CI [-2.95, 2.84], t(214) = -0.04, p = 0.968; Std. beta = -7.37e-03, 95% CI [-0.37, 0.35])
  • The effect of time point [2nd] is statistically non-significant and positive (beta = 0.98, 95% CI [-0.70, 2.67], t(214) = 1.14, p = 0.254; Std. beta = 0.12, 95% CI [-0.09, 0.33])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and negative (beta = -1.59, 95% CI [-4.03, 0.86], t(214) = -1.27, p = 0.203; Std. beta = -0.20, 95% CI [-0.50, 0.11])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

pss

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict pss with group and time_point (formula: pss ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.70) and the part related to the fixed effects alone (marginal R2) is of 7.55e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 45.21 (95% CI [42.25, 48.16], t(214) = 29.96, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and negative (beta = -0.35, 95% CI [-4.47, 3.76], t(214) = -0.17, p = 0.867; Std. beta = -0.03, 95% CI [-0.39, 0.33])
  • The effect of time point [2nd] is statistically non-significant and positive (beta = 2.09, 95% CI [-0.30, 4.49], t(214) = 1.71, p = 0.087; Std. beta = 0.18, 95% CI [-0.03, 0.39])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and negative (beta = -2.07, 95% CI [-5.54, 1.40], t(214) = -1.17, p = 0.242; Std. beta = -0.18, 95% CI [-0.48, 0.12])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

rki_responsible

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict rki_responsible with group and time_point (formula: rki_responsible ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.65) and the part related to the fixed effects alone (marginal R2) is of 1.15e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 20.95 (95% CI [19.88, 22.02], t(214) = 38.38, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.12, 95% CI [-1.37, 1.60], t(214) = 0.15, p = 0.878; Std. beta = 0.03, 95% CI [-0.33, 0.39])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.02, 95% CI [-0.95, 0.92], t(214) = -0.03, p = 0.975; Std. beta = -3.68e-03, 95% CI [-0.23, 0.22])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.25, 95% CI [-1.10, 1.60], t(214) = 0.37, p = 0.713; Std. beta = 0.06, 95% CI [-0.27, 0.39])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

rki_nonlinear

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict rki_nonlinear with group and time_point (formula: rki_nonlinear ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.61) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st, is at 13.12 (95% CI [12.37, 13.87], t(214) = 34.37, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.35, 95% CI [-0.69, 1.39], t(214) = 0.65, p = 0.513; Std. beta = 0.12, 95% CI [-0.24, 0.48])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.30, 95% CI [-0.99, 0.39], t(214) = -0.84, p = 0.398; Std. beta = -0.10, 95% CI [-0.34, 0.14])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.95, 95% CI [-0.05, 1.95], t(214) = 1.87, p = 0.061; Std. beta = 0.33, 95% CI [-0.02, 0.67])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

rki_peer

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict rki_peer with group and time_point (formula: rki_peer ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.58) and the part related to the fixed effects alone (marginal R2) is of 1.66e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 20.47 (95% CI [19.90, 21.03], t(214) = 71.39, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.21, 95% CI [-0.57, 0.99], t(214) = 0.53, p = 0.595; Std. beta = 0.10, 95% CI [-0.26, 0.46])
  • The effect of time point [2nd] is statistically non-significant and positive (beta = 0.03, 95% CI [-0.51, 0.57], t(214) = 0.11, p = 0.914; Std. beta = 0.01, 95% CI [-0.23, 0.26])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and negative (beta = -0.09, 95% CI [-0.87, 0.68], t(214) = -0.23, p = 0.818; Std. beta = -0.04, 95% CI [-0.40, 0.32])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

rki_expect

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict rki_expect with group and time_point (formula: rki_expect ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.50) and the part related to the fixed effects alone (marginal R2) is of 0.03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 4.60 (95% CI [4.33, 4.88], t(214) = 32.53, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.28, 95% CI [-0.10, 0.67], t(214) = 1.44, p = 0.150; Std. beta = 0.26, 95% CI [-0.09, 0.62])
  • The effect of time point [2nd] is statistically non-significant and positive (beta = 0.17, 95% CI [-0.12, 0.46], t(214) = 1.15, p = 0.252; Std. beta = 0.16, 95% CI [-0.11, 0.42])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.11, 95% CI [-0.31, 0.53], t(214) = 0.51, p = 0.609; Std. beta = 0.10, 95% CI [-0.28, 0.49])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

rki

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict rki with group and time_point (formula: rki ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.66) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st, is at 59.14 (95% CI [57.53, 60.75], t(214) = 71.87, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.96, 95% CI [-1.28, 3.20], t(214) = 0.84, p = 0.402; Std. beta = 0.15, 95% CI [-0.21, 0.51])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.12, 95% CI [-1.52, 1.28], t(214) = -0.16, p = 0.870; Std. beta = -0.02, 95% CI [-0.24, 0.21])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 1.25, 95% CI [-0.78, 3.27], t(214) = 1.21, p = 0.227; Std. beta = 0.20, 95% CI [-0.12, 0.53])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

raq_possible

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict raq_possible with group and time_point (formula: raq_possible ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.50) and the part related to the fixed effects alone (marginal R2) is of 0.01. The model’s intercept, corresponding to group = control and time_point = 1st, is at 15.74 (95% CI [15.28, 16.21], t(214) = 66.46, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and negative (beta = -0.16, 95% CI [-0.81, 0.49], t(214) = -0.49, p = 0.626; Std. beta = -0.09, 95% CI [-0.45, 0.27])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.39, 95% CI [-0.88, 0.09], t(214) = -1.58, p = 0.114; Std. beta = -0.22, 95% CI [-0.48, 0.05])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.70, 95% CI [-4.44e-03, 1.40], t(214) = 1.95, p = 0.051; Std. beta = 0.38, 95% CI [-2.45e-03, 0.77])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

raq_difficulty

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict raq_difficulty with group and time_point (formula: raq_difficulty ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.59) and the part related to the fixed effects alone (marginal R2) is of 3.83e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 12.53 (95% CI [12.18, 12.89], t(214) = 70.01, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and negative (beta = -0.23, 95% CI [-0.72, 0.26], t(214) = -0.92, p = 0.360; Std. beta = -0.17, 95% CI [-0.52, 0.19])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.12, 95% CI [-0.45, 0.21], t(214) = -0.71, p = 0.476; Std. beta = -0.09, 95% CI [-0.33, 0.15])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.21, 95% CI [-0.26, 0.69], t(214) = 0.88, p = 0.379; Std. beta = 0.16, 95% CI [-0.19, 0.51])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

raq

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict raq with group and time_point (formula: raq ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.59) and the part related to the fixed effects alone (marginal R2) is of 6.08e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 28.28 (95% CI [27.53, 29.02], t(214) = 74.25, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and negative (beta = -0.39, 95% CI [-1.43, 0.65], t(214) = -0.73, p = 0.463; Std. beta = -0.13, 95% CI [-0.49, 0.22])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.50, 95% CI [-1.20, 0.20], t(214) = -1.39, p = 0.164; Std. beta = -0.17, 95% CI [-0.41, 0.07])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.90, 95% CI [-0.11, 1.91], t(214) = 1.75, p = 0.080; Std. beta = 0.31, 95% CI [-0.04, 0.66])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

who

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict who with group and time_point (formula: who ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.67) and the part related to the fixed effects alone (marginal R2) is of 1.21e-04. The model’s intercept, corresponding to group = control and time_point = 1st, is at 14.62 (95% CI [13.47, 15.78], t(214) = 24.80, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.02, 95% CI [-1.58, 1.63], t(214) = 0.03, p = 0.976; Std. beta = 5.55e-03, 95% CI [-0.36, 0.37])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.11, 95% CI [-1.08, 0.87], t(214) = -0.21, p = 0.832; Std. beta = -0.02, 95% CI [-0.25, 0.20])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.06, 95% CI [-1.35, 1.47], t(214) = 0.08, p = 0.933; Std. beta = 0.01, 95% CI [-0.31, 0.33])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

phq

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict phq with group and time_point (formula: phq ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.78) and the part related to the fixed effects alone (marginal R2) is of 4.15e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 3.66 (95% CI [2.69, 4.62], t(214) = 7.45, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.20, 95% CI [-1.14, 1.54], t(214) = 0.29, p = 0.770; Std. beta = 0.05, 95% CI [-0.31, 0.42])
  • The effect of time point [2nd] is statistically non-significant and positive (beta = 0.12, 95% CI [-0.54, 0.78], t(214) = 0.35, p = 0.727; Std. beta = 0.03, 95% CI [-0.15, 0.21])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.35, 95% CI [-0.61, 1.31], t(214) = 0.71, p = 0.475; Std. beta = 0.09, 95% CI [-0.16, 0.35])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

gad

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict gad with group and time_point (formula: gad ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.71) and the part related to the fixed effects alone (marginal R2) is of 1.87e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 3.38 (95% CI [2.48, 4.28], t(214) = 7.39, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and negative (beta = -0.30, 95% CI [-1.54, 0.95], t(214) = -0.47, p = 0.639; Std. beta = -0.09, 95% CI [-0.44, 0.27])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.03, 95% CI [-0.74, 0.68], t(214) = -0.09, p = 0.931; Std. beta = -9.05e-03, 95% CI [-0.21, 0.19])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.43, 95% CI [-0.60, 1.46], t(214) = 0.82, p = 0.412; Std. beta = 0.12, 95% CI [-0.17, 0.42])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

nb_pcs

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict nb_pcs with group and time_point (formula: nb_pcs ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.68) and the part related to the fixed effects alone (marginal R2) is of 1.66e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 51.88 (95% CI [50.08, 53.68], t(214) = 56.38, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and negative (beta = -0.46, 95% CI [-2.96, 2.05], t(214) = -0.36, p = 0.722; Std. beta = -0.07, 95% CI [-0.42, 0.29])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.79, 95% CI [-2.29, 0.71], t(214) = -1.03, p = 0.304; Std. beta = -0.11, 95% CI [-0.33, 0.10])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.72, 95% CI [-1.45, 2.90], t(214) = 0.65, p = 0.516; Std. beta = 0.10, 95% CI [-0.21, 0.41])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

nb_mcs

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict nb_mcs with group and time_point (formula: nb_mcs ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.62) and the part related to the fixed effects alone (marginal R2) is of 3.48e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 50.20 (95% CI [48.02, 52.38], t(214) = 45.12, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.08, 95% CI [-2.95, 3.11], t(214) = 0.05, p = 0.959; Std. beta = 9.49e-03, 95% CI [-0.35, 0.37])
  • The effect of time point [2nd] is statistically non-significant and positive (beta = 0.96, 95% CI [-1.03, 2.94], t(214) = 0.94, p = 0.345; Std. beta = 0.11, 95% CI [-0.12, 0.35])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and negative (beta = -1.52, 95% CI [-4.39, 1.35], t(214) = -1.04, p = 0.300; Std. beta = -0.18, 95% CI [-0.52, 0.16])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

Likelihood ratio tests

outcome

model

npar

AIC

BIC

logLik

deviance

Chisq

Df

p

sets

null

3

930.640

940.821

-462.320

924.640

sets

random

6

933.086

953.448

-460.543

921.086

3.554

3

0.314

setv

null

3

816.873

827.054

-405.436

810.873

setv

random

6

820.430

840.791

-404.215

808.430

2.443

3

0.486

maks

null

3

1,142.673

1,152.854

-568.337

1,136.673

maks

random

6

1,147.452

1,167.814

-567.726

1,135.452

1.221

3

0.748

ibs

null

3

938.097

948.278

-466.048

932.097

ibs

random

6

938.933

959.295

-463.467

926.933

5.163

3

0.160

ers_e

null

3

737.774

747.955

-365.887

731.774

ers_e

random

6

738.664

759.025

-363.332

726.664

5.110

3

0.164

ers_r

null

3

795.164

805.345

-394.582

789.164

ers_r

random

6

797.269

817.630

-392.634

785.269

3.895

3

0.273

pss_pa

null

3

1,251.529

1,261.710

-622.765

1,245.529

pss_pa

random

6

1,252.589

1,272.951

-620.295

1,240.589

4.940

3

0.176

pss_ps

null

3

1,481.787

1,491.968

-737.894

1,475.787

pss_ps

random

6

1,485.709

1,506.071

-736.855

1,473.709

2.078

3

0.556

pss

null

3

1,637.610

1,647.790

-815.805

1,631.610

pss

random

6

1,640.212

1,660.573

-814.106

1,628.212

3.398

3

0.334

rki_responsible

null

3

1,199.197

1,209.378

-596.598

1,193.197

rki_responsible

random

6

1,204.864

1,225.225

-596.432

1,192.864

0.333

3

0.954

rki_nonlinear

null

3

1,056.679

1,066.860

-525.339

1,050.679

rki_nonlinear

random

6

1,056.392

1,076.754

-522.196

1,044.392

6.287

3

0.098

rki_peer

null

3

929.670

939.851

-461.835

923.670

rki_peer

random

6

935.375

955.736

-461.687

923.375

0.295

3

0.961

rki_expect

null

3

640.133

650.313

-317.066

634.133

rki_expect

random

6

638.201

658.563

-313.101

626.201

7.931

3

0.047

rki

null

3

1,382.908

1,393.089

-688.454

1,376.908

rki

random

6

1,384.639

1,405.001

-686.319

1,372.639

4.269

3

0.234

raq_possible

null

3

862.630

872.811

-428.315

856.630

raq_possible

random

6

864.464

884.826

-426.232

852.464

4.166

3

0.244

raq_difficulty

null

3

721.521

731.702

-357.761

715.521

raq_difficulty

random

6

726.353

746.715

-357.177

714.353

1.168

3

0.761

raq

null

3

1,054.792

1,064.973

-524.396

1,048.792

raq

random

6

1,057.641

1,078.002

-522.820

1,045.641

3.152

3

0.369

who

null

3

1,227.754

1,237.934

-610.877

1,221.754

who

random

6

1,233.694

1,254.056

-610.847

1,221.694

0.059

3

0.996

phq

null

3

1,113.855

1,124.036

-553.928

1,107.855

phq

random

6

1,117.737

1,138.099

-552.868

1,105.737

2.118

3

0.548

gad

null

3

1,106.251

1,116.432

-550.126

1,100.251

gad

random

6

1,111.083

1,131.445

-549.542

1,099.083

1.168

3

0.761

nb_pcs

null

3

1,422.312

1,432.493

-708.156

1,416.312

nb_pcs

random

6

1,427.223

1,447.585

-707.611

1,415.223

1.089

3

0.780

nb_mcs

null

3

1,520.431

1,530.612

-757.215

1,514.431

nb_mcs

random

6

1,525.076

1,545.438

-756.538

1,513.076

1.355

3

0.716

Post hoc analysis text

Table

outcome

time

control

treatment

between

n

estimate

within es

n

estimate

within es

p

es

sets

1st

58

19.02 ± 2.05

62

19.37 ± 2.05

0.347

-0.219

sets

2nd

53

18.93 ± 2.04

0.053

47

19.67 ± 2.02

-0.183

0.072

-0.455

setv

1st

58

11.03 ± 1.67

62

11.24 ± 1.67

0.496

-0.186

setv

2nd

53

11.35 ± 1.64

-0.285

47

11.32 ± 1.60

-0.071

0.925

0.028

maks

1st

58

44.67 ± 3.71

62

45.16 ± 3.71

0.471

-0.232

maks

2nd

53

44.44 ± 3.63

0.109

47

44.98 ± 3.50

0.088

0.456

-0.253

ibs

1st

58

15.41 ± 2.30

62

15.47 ± 2.30

0.898

-0.041

ibs

2nd

53

15.58 ± 2.26

-0.129

47

16.04 ± 2.17

-0.431

0.308

-0.343

ers_e

1st

58

12.14 ± 1.41

62

12.29 ± 1.41

0.556

-0.172

ers_e

2nd

53

11.85 ± 1.39

0.331

47

12.38 ± 1.35

-0.099

0.054

-0.602

ers_r

1st

58

11.02 ± 1.49

62

11.19 ± 1.49

0.517

-0.142

ers_r

2nd

53

11.12 ± 1.48

-0.080

47

11.55 ± 1.47

-0.290

0.141

-0.352

pss_pa

1st

58

44.47 ± 4.42

62

44.76 ± 4.42

0.717

-0.097

pss_pa

2nd

53

43.38 ± 4.36

0.359

47

44.21 ± 4.27

0.183

0.340

-0.273

pss_ps

1st

58

26.67 ± 8.08

62

26.61 ± 8.08

0.968

0.013

pss_ps

2nd

53

27.65 ± 7.92

-0.220

47

26.01 ± 7.60

0.136

0.290

0.370

pss

1st

58

45.21 ± 11.49

62

44.85 ± 11.49

0.867

0.056

pss

2nd

53

47.30 ± 11.25

-0.330

47

44.88 ± 10.80

-0.003

0.274

0.383

rki_responsible

1st

58

20.95 ± 4.16

62

21.06 ± 4.16

0.879

-0.047

rki_responsible

2nd

53

20.93 ± 4.08

0.006

47

21.30 ± 3.94

-0.097

0.646

-0.150

rki_nonlinear

1st

58

13.12 ± 2.91

62

13.47 ± 2.91

0.514

-0.190

rki_nonlinear

2nd

53

12.82 ± 2.86

0.163

47

14.12 ± 2.78

-0.359

0.022

-0.711

rki_peer

1st

58

20.47 ± 2.18

62

20.68 ± 2.18

0.596

-0.149

rki_peer

2nd

53

20.49 ± 2.15

-0.021

47

20.62 ± 2.10

0.043

0.777

-0.085

rki_expect

1st

58

4.60 ± 1.08

62

4.89 ± 1.08

0.151

-0.367

rki_expect

2nd

53

4.77 ± 1.07

-0.220

47

5.17 ± 1.05

-0.362

0.065

-0.509

rki

1st

58

59.14 ± 6.27

62

60.10 ± 6.27

0.404

-0.259

rki

2nd

53

59.02 ± 6.15

0.031

47

61.23 ± 5.94

-0.306

0.070

-0.596

raq_possible

1st

58

15.74 ± 1.80

62

15.58 ± 1.80

0.626

0.125

raq_possible

2nd

53

15.35 ± 1.79

0.303

47

15.89 ± 1.75

-0.236

0.133

-0.415

raq_difficulty

1st

58

12.53 ± 1.36

62

12.31 ± 1.36

0.361

0.260

raq_difficulty

2nd

53

12.41 ± 1.34

0.137

47

12.40 ± 1.31

-0.107

0.959

0.016

raq

1st

58

28.28 ± 2.90

62

27.89 ± 2.90

0.464

0.210

raq

2nd

53

27.78 ± 2.86

0.268

47

28.29 ± 2.78

-0.219

0.363

-0.278

who

1st

58

14.62 ± 4.49

62

14.65 ± 4.49

0.976

-0.009

who

2nd

53

14.51 ± 4.40

0.041

47

14.60 ± 4.24

0.018

0.922

-0.033

phq

1st

58

3.66 ± 3.74

62

3.85 ± 3.74

0.770

-0.115

phq

2nd

53

3.77 ± 3.64

-0.067

47

4.32 ± 3.45

-0.268

0.440

-0.315

gad

1st

58

3.38 ± 3.48

62

3.08 ± 3.48

0.639

0.159

gad

2nd

53

3.35 ± 3.41

0.017

47

3.48 ± 3.26

-0.213

0.844

-0.070

nb_pcs

1st

58

51.88 ± 7.01

62

51.42 ± 7.01

0.722

0.115

nb_pcs

2nd

53

51.09 ± 6.87

0.198

47

51.36 ± 6.61

0.017

0.844

-0.067

nb_mcs

1st

58

50.20 ± 8.47

62

50.28 ± 8.47

0.959

-0.015

nb_mcs

2nd

53

51.16 ± 8.34

-0.182

47

49.72 ± 8.08

0.107

0.382

0.274

Between group

sets

1st

t(191.28) = 0.94, p = 0.347, Cohen d = -0.22, 95% CI (-0.39 to 1.09)

2st

t(204.03) = 1.81, p = 0.072, Cohen d = -0.45, 95% CI (-0.07 to 1.54)

setv

1st

t(170.27) = 0.68, p = 0.496, Cohen d = -0.19, 95% CI (-0.39 to 0.81)

2st

t(189.13) = -0.09, p = 0.925, Cohen d = 0.03, 95% CI (-0.67 to 0.61)

maks

1st

t(154.38) = 0.72, p = 0.471, Cohen d = -0.23, 95% CI (-0.85 to 1.83)

2st

t(173.65) = 0.75, p = 0.456, Cohen d = -0.25, 95% CI (-0.88 to 1.94)

ibs

1st

t(155.14) = 0.13, p = 0.898, Cohen d = -0.04, 95% CI (-0.78 to 0.88)

2st

t(174.49) = 1.02, p = 0.308, Cohen d = -0.34, 95% CI (-0.42 to 1.33)

ers_e

1st

t(163.05) = 0.59, p = 0.556, Cohen d = -0.17, 95% CI (-0.36 to 0.66)

2st

t(182.62) = 1.94, p = 0.054, Cohen d = -0.60, 95% CI (-0.01 to 1.07)

ers_r

1st

t(199.48) = 0.65, p = 0.517, Cohen d = -0.14, 95% CI (-0.36 to 0.71)

2st

t(208.55) = 1.48, p = 0.141, Cohen d = -0.35, 95% CI (-0.15 to 1.02)

pss_pa

1st

t(172.87) = 0.36, p = 0.717, Cohen d = -0.10, 95% CI (-1.30 to 1.88)

2st

t(191.28) = 0.96, p = 0.340, Cohen d = -0.27, 95% CI (-0.88 to 2.53)

pss_ps

1st

t(152.06) = -0.04, p = 0.968, Cohen d = 0.01, 95% CI (-2.98 to 2.86)

2st

t(171.01) = -1.06, p = 0.290, Cohen d = 0.37, 95% CI (-4.71 to 1.42)

pss

1st

t(151.97) = -0.17, p = 0.867, Cohen d = 0.06, 95% CI (-4.50 to 3.80)

2st

t(170.90) = -1.10, p = 0.274, Cohen d = 0.38, 95% CI (-6.78 to 1.94)

rki_responsible

1st

t(158.11) = 0.15, p = 0.879, Cohen d = -0.05, 95% CI (-1.38 to 1.62)

2st

t(177.68) = 0.46, p = 0.646, Cohen d = -0.15, 95% CI (-1.22 to 1.96)

rki_nonlinear

1st

t(163.55) = 0.65, p = 0.514, Cohen d = -0.19, 95% CI (-0.70 to 1.40)

2st

t(183.10) = 2.30, p = 0.022, Cohen d = -0.71, 95% CI (0.19 to 2.41)

rki_peer

1st

t(167.06) = 0.53, p = 0.596, Cohen d = -0.15, 95% CI (-0.58 to 1.00)

2st

t(186.34) = 0.28, p = 0.777, Cohen d = -0.09, 95% CI (-0.72 to 0.96)

rki_expect

1st

t(178.45) = 1.44, p = 0.151, Cohen d = -0.37, 95% CI (-0.10 to 0.67)

2st

t(195.58) = 1.86, p = 0.065, Cohen d = -0.51, 95% CI (-0.02 to 0.81)

rki

1st

t(157.65) = 0.84, p = 0.404, Cohen d = -0.26, 95% CI (-1.30 to 3.22)

2st

t(177.19) = 1.82, p = 0.070, Cohen d = -0.60, 95% CI (-0.18 to 4.60)

raq_possible

1st

t(178.10) = -0.49, p = 0.626, Cohen d = 0.12, 95% CI (-0.81 to 0.49)

2st

t(195.32) = 1.51, p = 0.133, Cohen d = -0.42, 95% CI (-0.16 to 1.23)

raq_difficulty

1st

t(165.68) = -0.92, p = 0.361, Cohen d = 0.26, 95% CI (-0.72 to 0.26)

2st

t(185.10) = -0.05, p = 0.959, Cohen d = 0.02, 95% CI (-0.54 to 0.51)

raq

1st

t(165.12) = -0.73, p = 0.464, Cohen d = 0.21, 95% CI (-1.43 to 0.66)

2st

t(184.58) = 0.91, p = 0.363, Cohen d = -0.28, 95% CI (-0.60 to 1.63)

who

1st

t(155.37) = 0.03, p = 0.976, Cohen d = -0.01, 95% CI (-1.60 to 1.64)

2st

t(174.74) = 0.10, p = 0.922, Cohen d = -0.03, 95% CI (-1.62 to 1.79)

phq

1st

t(141.56) = 0.29, p = 0.770, Cohen d = -0.11, 95% CI (-1.15 to 1.55)

2st

t(157.62) = 0.77, p = 0.440, Cohen d = -0.32, 95% CI (-0.85 to 1.95)

gad

1st

t(150.36) = -0.47, p = 0.639, Cohen d = 0.16, 95% CI (-1.56 to 0.96)

2st

t(169.00) = 0.20, p = 0.844, Cohen d = -0.07, 95% CI (-1.19 to 1.45)

nb_pcs

1st

t(154.18) = -0.36, p = 0.722, Cohen d = 0.11, 95% CI (-2.98 to 2.07)

2st

t(173.42) = 0.20, p = 0.844, Cohen d = -0.07, 95% CI (-2.40 to 2.93)

nb_mcs

1st

t(162.25) = 0.05, p = 0.959, Cohen d = -0.02, 95% CI (-2.98 to 3.14)

2st

t(181.85) = -0.88, p = 0.382, Cohen d = 0.27, 95% CI (-4.68 to 1.80)

Within treatment group

sets

1st vs 2st

t(112.09) = 0.92, p = 0.360, Cohen d = -0.18, 95% CI (-0.34 to 0.93)

setv

1st vs 2st

t(108.43) = 0.35, p = 0.724, Cohen d = -0.07, 95% CI (-0.36 to 0.52)

maks

1st vs 2st

t(105.59) = -0.44, p = 0.664, Cohen d = 0.09, 95% CI (-1.03 to 0.66)

ibs

1st vs 2st

t(105.73) = 2.13, p = 0.035, Cohen d = -0.43, 95% CI (0.04 to 1.10)

ers_e

1st vs 2st

t(107.16) = 0.49, p = 0.623, Cohen d = -0.10, 95% CI (-0.27 to 0.44)

ers_r

1st vs 2st

t(113.61) = 1.47, p = 0.145, Cohen d = -0.29, 95% CI (-0.13 to 0.85)

pss_pa

1st vs 2st

t(108.88) = -0.91, p = 0.364, Cohen d = 0.18, 95% CI (-1.75 to 0.65)

pss_ps

1st vs 2st

t(105.16) = -0.67, p = 0.504, Cohen d = 0.14, 95% CI (-2.40 to 1.19)

pss

1st vs 2st

t(105.14) = 0.02, p = 0.987, Cohen d = -0.00, 95% CI (-2.52 to 2.57)

rki_responsible

1st vs 2st

t(106.27) = 0.48, p = 0.633, Cohen d = -0.10, 95% CI (-0.75 to 1.23)

rki_nonlinear

1st vs 2st

t(107.25) = 1.78, p = 0.078, Cohen d = -0.36, 95% CI (-0.07 to 1.39)

rki_peer

1st vs 2st

t(107.87) = -0.22, p = 0.830, Cohen d = 0.04, 95% CI (-0.63 to 0.50)

rki_expect

1st vs 2st

t(109.84) = 1.81, p = 0.073, Cohen d = -0.36, 95% CI (-0.03 to 0.59)

rki

1st vs 2st

t(106.19) = 1.51, p = 0.134, Cohen d = -0.31, 95% CI (-0.35 to 2.61)

raq_possible

1st vs 2st

t(109.78) = 1.18, p = 0.240, Cohen d = -0.24, 95% CI (-0.21 to 0.82)

raq_difficulty

1st vs 2st

t(107.62) = 0.53, p = 0.595, Cohen d = -0.11, 95% CI (-0.26 to 0.44)

raq

1st vs 2st

t(107.52) = 1.09, p = 0.279, Cohen d = -0.22, 95% CI (-0.33 to 1.15)

who

1st vs 2st

t(105.77) = -0.09, p = 0.931, Cohen d = 0.02, 95% CI (-1.08 to 0.99)

phq

1st vs 2st

t(103.14) = 1.32, p = 0.191, Cohen d = -0.27, 95% CI (-0.24 to 1.17)

gad

1st vs 2st

t(104.84) = 1.05, p = 0.297, Cohen d = -0.21, 95% CI (-0.36 to 1.15)

nb_pcs

1st vs 2st

t(105.55) = -0.08, p = 0.935, Cohen d = 0.02, 95% CI (-1.66 to 1.53)

nb_mcs

1st vs 2st

t(107.02) = -0.53, p = 0.596, Cohen d = 0.11, 95% CI (-2.67 to 1.54)

Within control group

sets

1st vs 2st

t(103.77) = -0.28, p = 0.782, Cohen d = 0.05, 95% CI (-0.70 to 0.53)

setv

1st vs 2st

t(102.07) = 1.48, p = 0.142, Cohen d = -0.28, 95% CI (-0.11 to 0.74)

maks

1st vs 2st

t(100.87) = -0.57, p = 0.572, Cohen d = 0.11, 95% CI (-1.04 to 0.58)

ibs

1st vs 2st

t(100.93) = 0.67, p = 0.505, Cohen d = -0.13, 95% CI (-0.33 to 0.68)

ers_e

1st vs 2st

t(101.52) = -1.72, p = 0.089, Cohen d = 0.33, 95% CI (-0.63 to 0.05)

ers_r

1st vs 2st

t(104.54) = 0.42, p = 0.676, Cohen d = -0.08, 95% CI (-0.37 to 0.57)

pss_pa

1st vs 2st

t(102.27) = -1.87, p = 0.065, Cohen d = 0.36, 95% CI (-2.24 to 0.07)

pss_ps

1st vs 2st

t(100.70) = 1.14, p = 0.256, Cohen d = -0.22, 95% CI (-0.73 to 2.69)

pss

1st vs 2st

t(100.69) = 1.71, p = 0.090, Cohen d = -0.33, 95% CI (-0.33 to 4.52)

rki_responsible

1st vs 2st

t(101.15) = -0.03, p = 0.975, Cohen d = 0.01, 95% CI (-0.96 to 0.93)

rki_nonlinear

1st vs 2st

t(101.56) = -0.84, p = 0.401, Cohen d = 0.16, 95% CI (-1.00 to 0.40)

rki_peer

1st vs 2st

t(101.82) = 0.11, p = 0.914, Cohen d = -0.02, 95% CI (-0.51 to 0.57)

rki_expect

1st vs 2st

t(102.70) = 1.15, p = 0.255, Cohen d = -0.22, 95% CI (-0.12 to 0.46)

rki

1st vs 2st

t(101.12) = -0.16, p = 0.871, Cohen d = 0.03, 95% CI (-1.53 to 1.30)

raq_possible

1st vs 2st

t(102.67) = -1.58, p = 0.118, Cohen d = 0.30, 95% CI (-0.88 to 0.10)

raq_difficulty

1st vs 2st

t(101.72) = -0.71, p = 0.478, Cohen d = 0.14, 95% CI (-0.45 to 0.21)

raq

1st vs 2st

t(101.68) = -1.39, p = 0.167, Cohen d = 0.27, 95% CI (-1.20 to 0.21)

who

1st vs 2st

t(100.95) = -0.21, p = 0.832, Cohen d = 0.04, 95% CI (-1.09 to 0.88)

phq

1st vs 2st

t(99.90) = 0.35, p = 0.728, Cohen d = -0.07, 95% CI (-0.55 to 0.79)

gad

1st vs 2st

t(100.57) = -0.09, p = 0.931, Cohen d = 0.02, 95% CI (-0.75 to 0.69)

nb_pcs

1st vs 2st

t(100.86) = -1.03, p = 0.307, Cohen d = 0.20, 95% CI (-2.31 to 0.73)

nb_mcs

1st vs 2st

t(101.46) = 0.94, p = 0.348, Cohen d = -0.18, 95% CI (-1.05 to 2.97)

Plot